capitulo 8

8
How to Provide Accurate and Robust
Traffic Forecasts Practically?
Yang Zhang
Shanghai Municipal Transportation
Information Center
China
1. Introduction
With the development of our modern cities, growing traffic problems adversely affect
people’s traveling convenience more and more, which has become one of the most crucial
factors considered in urban planning and design in recent years. Urban traffic congestion is
a severe problem that significantly reduces the quality of life in particularly metropolitan
areas. However, frequently constructing new roads is not realistic and untenable in social
and economic aspects. In the effort to deal with this intractable problem, so-called intelligent
transportation systems (ITS) technologies are successfully implemented widely throughout
the world nowadays. ITS with two important components advanced traffic management
systems (ATMS) and advanced traveler information systems (ATIS) aim to relieve the
increasing congestion and decrease travel time through providing information to the drivers
by means of radio broadcasts or dynamic route guidance systems.
The provision of accurate real-time information and predictions of traffic states such as
traffic flow, travel time, occupancies, etc., is much fundamental and contributive to the great
success of ITS (Chen et al., 2010; Dong et al., 2010; Vlahogianni et al., 2004; Lam et al., 2006;
Tan et al., 2009; Tang et al., 2003; Thomas et al., 2010; Zhang & Liu, 2008, 2009c). As an
important part of ITS, traffic states analysis and traffic forecasting are important in directing
commuters to pick optimal routes, which have attracted many researchers to focus on this
subject in recent decades. In general, as illustrated in the statement, the traffic forecasting
“can be separated into two paradigms: the empirical based, incorporating fairly standard
statistical methodology on the one hand, and that based on traffic process theory, either of
demand or of supply, on the other” (Van Arem et al., 1997).
Because of the feasibility of data collection from numerous kinds of equipments and the
requirements of dynamic management, the empirical approaches for traffic forecasting
correspond with the development trends of ITS. It aims to find out the hidden regularity of
traffic states through the random and uncertain traffic data by systematic analysis and a
variety of mathematics/physics methods.
The empirical approaches can be approximately divided into two types: basic forecasts
approaches and combined forecasts approaches. The basic forecasts approach means to
predict the traffic state using a certain particular prediction model. The robustness and
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accuracy of these approaches lie on the prediction models themselves. Furthermore, basic
forecasts approaches can be roughly classified into two types: parametric and
nonparametric techniques. Both techniques have shown their own advantages on different
occasions in recent years (Tsekeris & Stathopoulos, 2010; Zhang & Liu, 2009f, Zhang & Liu,
2010; Zhang et al., 2010). On the basis of the classification, the chapter provides a systematic
review of these models such as historical-mean (HM), filtering algorithm, linear and
nonlinear regression, autoregressive process, neural network (NN), fuzzy systems, support
vector regression (SVR), and Bayesian networks, etc.
The combined forecasts approach means to combine different forecasts into a single one that
is assumed to produce a more accurate forecast. The robustness and accuracy of combined
forecast approaches lie not only on the prediction effect of individual prediction model, but
also on the efficiency of combination. Because the combined method is to apply each
predictor’s unique feature to capture different patterns in the data, it would give a smaller
error variance than any of the individual methods (Bates & Granger, 1969). This advantage
may make the approach fully scalable to the very large amounts of traffic data practically.
Due to its simplicity and practicability, the combined forecasts approach becomes very
important to traffic forecasting, and researchers have focused on it, both theoretical and
applied.
Though the data-driven traffic forecasting gains many achievements, there still exist some
unsolved problems. From the practical point of view, data gained from some detectors are
incomplete, i.e., partially or completely missing or substantially contaminated by noises. The
missing data sometimes render an entire dataset useless, which is a major hurdle in
analyzing traffic information. As missing data treatment is an important preparation step
for effective management of ITS, some proper solutions to solve missing data problems are
provided in the chapter. And the chapter ends with a brief introdcution of Shanghai
Integrated Transportation Information Platform (SITIC), which represents the level of
informatization development in transportation.
2. A brief review of data-driven traffic forecasting
The data-driven traffic forecasting refers to predicting the future state of a certain
transportation system based on the historical data, existing traffic data and the related
statistics data (Brockwell & Davis, 2002; Chrobok, 2004). Traffic forecasting is a branch of
forecasting, and it is an important part of modern transportation planning and intelligent
transportation system. Usually, traffic flow, average speed and travel time etc., are defined
as the basic parameters of traffic state. Specifically, traffic forecasting is essentially the
prediction of these basic parameters based on dynamic road traffic time series data. For
instance, most of literature foucs on traffc flow forecasting (Jiang & Adeli, 2004; Qiao et al.,
2001; Abdulhai et al., 1999; Castillo et al., 2008; Chen & Chen, 2007; Dimitriou et al., 2008;
Ding et al., 2002; Huang & Sadek, 2009; Ghosh et al., 2005, 2007; Smith et al., 2002), travel
time forecasting, and related analysis such as validation, optimization, etc. (Chan et al., 2003;
Chan & Lam, 2005; Chang et al., 2010; Kwon, 2000; Kwon & Petty, 2005; Lam, 2008; Lam et
al., 2002, 2005, 2008; Lam & Chan, 2004; Lee et al. 2009; Nath et al., 2010; Schadschneider et
al., 2005; Tam & Lam, 2009; Tang & Lam, 2001; Yang et al., 2010).
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Overall, the process of traffic state variation is a real-time, nonlinear, high dimensional and
non-stationary stochastic process. With the shortening of statistical time range, the stochastic
and uncertainty of traffic state are more and more strong. Short-term traffic state variation is
not only related to the state of the local road section over the past few hours, but also
influenced by the traffic states of upstream and downstream road sections, weather
situation and unexpected events, etc.
From the spatial and temporal point of view, the traffic state can reflect regular variation.
For example, the traffic states of various road sections of urban road network during peak
and non-peak period show periodic variation respectively; and the traffic states in urban
highway traffic on weekdays and weekends also show different periodic variation, which
reflects the temporal regularity of road network traffic. Meanwhile, the urban road network
topology, the length of each road link, lane width and traffic direction, etc. can determine
the variation of traffic state on a particular road link, which reflects the spatial regularity of
road network traffic. Therefore, in the research of transportation prediction, it is essential to
fully consider real-time traffic state variation with the randomness and regularity
temporally and spatially. Namely, real-time traffic forecasting should predict the future
traffic state on the basis of studying the specific sections of the historical traffic data, the
whole spatial-temporal road network traffic condition variation, weather situation, and
other influence factors. Fig.1 describes the framework of data-driven traffic forecasting
models.
Historical Traffic
Information
Real-time Traffic
Information
Topological Structure of
Urban Road Network
Topological Structure of
Highway
Positional Information
of Sensors
Traffic Predictive
Modeling
Travelers’
Behaviour
Data Collection Data Analysis
Decision Support
System
Information
Distribution Platform
Traffic Forecasts
Fig. 1. The framework of traffic forecasting models.
3. Traffic forecasting approaches
The following factors are usually used for the classification of traffic forecasting approaches:
single road link or transportation network, freeways or urban streets, physical models or
mathematical methodologies, univariate or multivariate method, etc. From the methodology
point of view, the traffic forecasting approaches can be divided into two types: the empirical
based approaches and traffic process theory based approaches. For the convenience of data
collection from numerous kinds of equipments, a large amount of the historical traffic
information and real-time traffic information can be obtained. And the empirical approaches
become the new trend of ITS. In this part, we focus on the achievements concerned with
empirical approach according to its classification.
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3.1 Basic forecasts approaches
A large amount of scientific literature has been concerned with basic forecasts approaches.
On the basis of the classification, the chapter provides a systematic review of parametric and
nonparametric traffic forecasting techniques briefly.
3.1.1 Parametric traffic forecasting approaches
Since the early 1980s, extensive variety of parametric approaches has been employed
ranging from historical average algorithms (Smith & Demetsky, 1997; Wu et al., 2004),
smoothing techniques (Smith & Demetsky, 1997; Williams et al., 1998), linear and nonlinear
regression (Deng et al., 2009; Lu et al., 2009; Zhang & Rice 2003; Sun et al., 2003), filtering
techniques (Ross, 1982; Okutani & Stephanedes, 1984; Whittaker et al., 1997; Chien &
Kuchipudi, 2003; Stathopoulos & Karlaftis, 2003), to autoregressive linear processes (Min et
al., 2010; Min & Wynter, 2011). Thereinto, the autoregressive integrated moving average
(ARIMA) (Ahmed & Cook, 1979) family of models such as simple ARIMA (Levin & Tsao,
1980; Nihan & Holmesland, 1980; Hamed et al., 1995; Smith, 1995; Williams, 1999),
ATHENA (Kirby et al., 1997), subset ARIMA (Lee & Fambro, 1999), SARIMA family (Smith
et al., 2002; Williams et al., 1998, 2003; Ghosh et al., 2005), are classical milestones in
forecasting area. Such time series methods belong to time domain approaches, and
frequency domain approaches like spectral analysis, “which are regressions on periodic
sines and cosines, show their important insights into traffic data which may not apparent in
an analysis in the time domain only” (Stathopoulos & Karlaftis, 2001a, b). The parametric
traffic forecasting approach is the milestone of the traditional time series forecasting. And it
brings significant developments for traffic forecasting.
3.1.2 Nonparametric traffic forecasting approaches
Lately extraordinary development of distinct nonparametric techniques, including
nonparametric regression, neural networks, etc., has shown that they may be able to become
a high potential alternative to their parametric counterparts (Huisken, 2003; Lam et al.,
2006). In essence, nonparametric statistical regression can be regarded as a dynamic
clustering model that relies on the relationship between dependent and independent traffic
variables. (Davis & Nihan, 1991; Smith & Demetsky, 1997; You & Kim, 2000; Smith et al.,
2000, 2002; Clark, 2003; Turochy, 2006). In other words, it attempts to identify past
information that are similar to the state at prediction time, which leads to easily
implemented nature. Over the past decade, another nonparametric technique, artificial
neural networks (ANNs) have been applied in traffic forecasting because of their strong
ability to capture the indeterministic and complex nonlinearity of time series (Smith &
Demetsky, 1994, 1997; Chang & Su, 1995; Dougherty & Cobbet, 1997; Lam & Xu, 2000; Park
et al., 1999; Dharia & Adeli, 2003; Wei et al., 2009; Wei & Lee 2007; Lee, 2009). Motivated by
the universal approximation property, neural network models ranging from purely static to
highly dynamic structures include the multilayer perceptrons (MLPs) (Clark et al., 1993;
Vythoulkas, 1993; Lee & Fambro, 1999; Gilmore & Abe, 1995; Ledoux, 1997; Innamaa, 2000;
Florio & Mussone, 1996; Yun et al., 1998; Zhang, 2000; Chen et al., 2001), the radial basis
function (RBF) ANNs (Lyons et al., 1996; Park et al., 1998; Park & Rilett, 1998; Chen et al.,
2001), the time-delayed ANNs (Lingras et al., 2000; Lingras & Mountford, 2001; Yun et al.,
1998; Yasdi 1999; Abdulhai et al., 1999; Dia, 2001; Ishak & Alecsandru, 2003), the recurrent
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ANNs (Dia, 2001; Van Lint et al., 2002, 2005), and the hybrid ANNs (Abdulhai et al., 1999;
Chen et al., 2001; Lingras & Mountford, 2001; Park, 2002; Yin et al., 2002; Vlahogianni et al.,
2005; Jiang & Adeli, 2005; Quek et al., 2006), etc. Besides the above neural networks models,
computational intelligence (CI) techniques that encompass fuzzy systems, machine learning
and evolutionary computation have been successfully developed in the field of traffic
forecasting. For instance, some literature applies Bayesian networks (Zhang et al. , 2004;
Castillo et al., 2008) and Bayesian inference based regression techniques (Khan, 2011;
Tebaldi et al., 2002; Sun et al., 2005, 2006; Zheng et al., 2006; Ghosh et al., 2007), some
literature uses fuzzy systems or fuzzy NNs to predict the traffic states (Dimitriou et al., 2008;
Quek et al., 2009). While others start to explore support vector regression (SVR) to model
traffic characteristics and produce prediction of traffic states (Castro-Neto, 2009; Ding et al.,
2002; Hong, 2011; Hong et al., 2011; Wu et al., 2004; Vanajakshi & Rilett, 2004). The recent
application of different CI techniques and hybrid intelligent systems has shown that the
rapidly expanding research field is promising.
3.2 Combined forecasts approaches
The basic idea of the combined forecasts approach is to apply each predictor’s unique
feature to capture different patterns in the data (Zhang & Liu, 2009d, 2009e). The
complement in capturing patterns of data sets is theoretically essential for more accurate
prediction (Timmermann, 2005; Huang, 2007). “Both theoretical and empirical findings
suggest that combining different methods can be an effective way to improve forecast
performances.” (Yu et al., 2005a). The linear combining forecasts methodology has a long
historical background. Compared to computational intelligence based nonlinear ensemble
forecasting models (Chen & Zhang, 2005; Chen & Chen, 2007), the linear combination
retains the conceptual and computational simplicity. In the part, we focus on the application
of linear combination method. Researchers have proposed various combined methods since
the pioneering work of Bates and Granger. Clemen provided a review and annotated
bibliography of the literature for reference (Clemen, 1989). “Research in various fields has
strongly suggested that the performance of prediction can be enhanced when (sometimes
even in simple fashion) forecasts are combined.” (Yang, 2004).
Basically, we can describe the main problem of combined forecasts as follows. Suppose there
are N forecasts such as VP1 (t),  VP2 (t), …,  VPN (t) (including correlated or uncorrelated
forecasts), where VPi (t) represents the forecasting result obtained from the ith model
during the time interval t. The combination of the different forecasts into a single forecast
VP (t) is assumed to produce a more accurate forecast. The general form of such a combined
forecast can be described with formula
  N
( ) i ( ) i= VP t = w VPi t (1)
1
where wi denotes the assigned weight of VPi (t), and commonly the sum of the weights is
equal to one, i.e., Σiwi=1. Our studies mainly investigate the combined models with the
additional restriction that no individual weight can be outside the interval [0, 1]. Various
methods can be applied to determine the weights used in the combined forecasts. Four
common methods are presented in the following.
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3.2.1 Equal Weights (EW) methods
The EW method, applying a simple arithmetic average of the individual forecasts, is a
relatively robust method with low computational efforts. Namely, each wi is equal to 1/N
(i=1, 2, …, N), where N is the number of forecasts. The beauty of using the simple average is
that it is easy to understand and implement, not requiring any estimation of weights or
other parameters (Jose & Winkler, 2008). This makes it robust because they are not sensitive
to estimation errors, which can sometimes be substantial. It often provides better results
than more complicated and sophisticated combining models (Clemen, 1989). Although the
approach has non-optimal weights, it may give rise to better results than time-varying
weights that are sometimes adversely affected by some unsystematic changes over time.
Under the circumstances, the method has the virtues of impartiality, robustness and a good
“track-record” in time series forecasting. It has been consistently the choice of many
researchers in the combination of forecasts.
3.2.2 Optimal Weights (OW) methods
Bates & Granger proposed that using a MV criterion can determine the weights to
adequately apply the additional information hidden in the discarded forecast(s) (Bates &
Granger, 1969), and Dickinson extended the method to the combinations of N forecasts
(Dickinson, 1973). Assuming that the individual forecast errors are unbiased, we can
calculate the vector of weights to minimize the error variance of the combination according
to the formula
'
= 1 ( 1 ) 1 - -
w M I I M I
n n n
-
V V
(2)
where In is the n×1 matrix with all elements unity (i.e. n×1 unit vector) and MV is the
covariance matrix of forecast errors (e.g. MVij is the covariance between the error of forecast i
and forecast j at a particular point in time). Granger & Ramanathan pointed that the method
is equivalent to a least squares regression in which the constant is suppressed and the
weights are constrained to sum to one (Granger & Ramanathan, 1984). In the case of a
combination of two forecasts, we suppose there is no correlation between forecast errors.
3.2.3 Minimum Error (ME) methods
The ME method minimizes the forecasting errors when combining individual forecasts into
a single one. A solution for this method applies linear programming (LP) whose principle
and computational process are described as follows (Yu et al., 2005a). Set the sum of
absolute forecasting error (i.e., Σi Ei(t) during the time interval t) as
  N N
LP | i ( )| i ( ) ( ) ( ) 1, 2, , T i= i= F = E t = w t VPi t VO t t =  (3)
1 1
where FLP is the objective function of LP; VO(t) denotes the observed value during the time
interval t and T the number of forecasting periods. To eliminate the absolute sign of the
objective function, assume that
  
| ( )| ( ) ( ), ( ) 0
( )
E t E t E t E t
i i i i
  
2 0, ( ) 0
i
i
u t = =
E t
,
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  
| ( )| ( ) 0, ( ) 0
( )
E t E t E t
i i i
  
2 ( ) ( ) 0
i
i i
v t = =
E t E t
(4)
The introduction of ui(t) and vi(t) aims to transform the absolute sign of the objective
function so as to be consistent with the standard form of LP. Obviously, |ei(t)|=ui(t)+vi(t),
ei(t)=ui(t)−vi(t). On the basis of the above specification, the LP model can be constructed as
follows:
 

N
 
1
 
Min O= u t v t
N
1
N
1
() ( ) ,
i= i i
V V
( ) ( ) ( ) ( ) ( ) 0,
w t t t u t v t =
Pi O
i= i i i
( ) 1,
w t =
i= i


( ) 0, ( ) 0, ( ) 0, 1, 2, , N, 1, 2, , T
w t u t v t i= t=
i i i

   
   
 
(5)
where i denotes the number of individual forecasts, and t represents the forecasting
periods. In the equation group, assuming wi≥0 aims to make every forecast method
contribute to the combined forecasting results. The ME method is equivalent to a simple
dynamic linear programming problem; thus, the optimal solutions to the LP can be
obtained by the simplex algorithm. The method is an effective combination methodology
with time-variant weights.
3.2.4 Minimum Variance (MV) methods
The linear combining forecasts methodology has a long historical background. Researchers
Since the negative value of the weight has no factual meaning, researchers usually add the
restriction that no individual weight can be outside the interval [0, 1] practically. The main
ideas are described as
T
( ),
Min w w
 
N
1
1, 1, 2, , N
0,
i V i
i= i
i
w = i=
w

 
M
(6)
where MV is the matrix of error variance. By solving the quadratic programming (QP)
problems, an optimal weight set can be obtained for the combining forecasts (Yu et al.,
2005b). The problem with this optimizing approach is that it still requires MV to be properly
estimated. Practically, MV is often not stationary, in which case it is estimated on the basis of
a short history of forecasts and thus the method becomes an adaptive approach to
combining forecasts (De Menezes et al., 2000).
4. Data imputation
Various imputation techniques have been developed in the past decade. Techniques including
naïve imputation, expectation maximization (EM) algorithm (Schafer, 1997; Dempster et al.,
1977), data augmentation (DA) algorithm (Tanner & Wong, 1987), and regression imputation,
etc. lead logically to modern approaches. Regression imputation and MI have been proved to
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be more effective than the others, especially the latter one (Ni et al., 2005; Zhong et al., 2004).
State space methodology is found to be extremely significant to ensure more accurate results in
nearest nonparametric regression (Kamarianakis & Prastakos, 2005). The amelioration
including the historical information in the state space may further improve imputation
accuracy. Zhang & Liu proposed LS-SVMs method incorporating with the multivariate state
space approach to recover missing traffic flow data in arterial streets of Xuhui district,
Shanghai (Zhang & Liu, 2009a, 2009b). The state space not only incorporates lagged values but
also is supplemented with aggregate measures such as historical information, spatial
information, etc. Fully applying spatial and temporal information, the state space based
approaches can model the traffic flow successfully. In this part, we focus on the imputation
techniques based on state space method (Zhang & Liu, 2009a, 2009b).
In time series, state is defined as a series of system values measured during the past k
intervals (kN). Measurements at time t, t-1,…, t-k compose a state vector and k is an
appropriate number of lags. A state vector of traffic flow measured by loop detector(s) l
every F minute(s) can be described by:
Xl(t, kl )= Vl(t),Vl(t  1),,Vl(t  kl ) (7)
where Vl(t) denotes the traffic flow from the detector(s) l during the time interval t; Vl(t-1)
represents the traffic flow from the detector(s) l during the previous F-minute interval, etc. If
L loop detectors are considered around the object detector(s) in the traffic network, the
values of l range from 1 to L (L N). When t≤kl, Xl(t, kl) contains the last kl-t+1 parameters
measured in theday before the chosen particular day. Object detector(s) can be defined as
the detector(s) with missing data.
Considering the historical information in the past week(s), the state space X(t, L) can be
defined as:
X(t,L)= X1(t, k1 ), X2 (t, k2 ),, XL(t, kL ),VGhist ,w(t) (8)
where VGhist,w(t) is the historical traffic flow from the object detector(s) at the time-of-day and
day-of-the-week associated with time interval t at week w that is usually selected as the
previous week. The selection of appropriate L and kl for each detector l is based on the
spatio-temporal analysis of traffic flows collected from loop detectors at different
intersections. Input-output pairs for the training process can apply the vectors
  (t,L),VG(t) X ,  ,T t kmax , kmax =max(k1 , k2 ,, kL ) (9)
where VG(t) is the traffic flow obtained from the object detector(s) G during the time interval
t; T is the number of time intervals in a day; kmax denotes the maximum value among the lags
kl, l=1, 2,…, L. This training process must suppose the good condition of detector(s) G and
close relation between VG(t) and Vl(t). The total number of training samples is (T-kmax+1).
When detector(s) G cannot supply complete data VG(T+h) at time T+h, h N, due to some
malfunctions, vectors X(T+h, L) are used as input variables to obtain the predicted results
VG (T+h) that can replace the missing values. Comparison between the imputed values and
the actual ones VG(T+h) can be utilized to verify the efficiency of different imputation
methods.
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5. A brief review of Shanghai integrated transportation information platform
In recent years, we have been exploring traffic informatization and building Shanghai
Integrated Transportation Information Platform (SITIC) that provides a mechanism to
connect isolated islands of information. After three periods of construction, the system
software/hardware, backbone networks, information distribution channels have been
completed successfully. The guiding thought for the development of SITIC is “Investigating
the present state, revealing the objective laws, and guiding the urban transport more
scientifically and efficiently”.
Classifying the transportation into Road Traffic, Public Traffic, Inter-city Traffic and
District/Transport Hub, sorts of information of vehicles and people were collected from
kinds of sources, which is the basis of the normal running of SITIC and further data mining.
Different real-time information on the transportation of Shanghai can be clearly shown in
SITIC. Researching the transportation problems in metropolis, especially the traffic
prediction, we found that mastering the situation of transportation is important to traffic
management, which leads to the essentiality of level division of the road network into macro
(network), meso (district), and micro (link) levels. Meanwhile, data gained from some
detectors are incomplete, i.e., partially or completely missing or substantially contaminated
by noises. This may be caused by malfunctions in data collection and recording systems that
often occur in practice. The missing data sometimes render an entire dataset useless, which
is a major hurdle in analyzing traffic information. Missing data treatment is another
important preparation step for effective management of intelligent transportation systems
(ITS). The following figure describes the contents and function of the platform briefly.
Fig. 2. The main structure of SITIC.
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6. Conclusion
The chapter summarizes data driven approaches for traffic prediction in three parts. First,
on the basis of classification of the main methods for traffic forecasting, the chapter aims to
describe a large amount of literature of traffic forecasting models. And we focus on the
decription of combined forecasts approaches that we believe represent the trend of the
development of traffic forecasting in practice. Second, from the practical point of view,
proper solutions to solve missing data problems are decribled, espertially the state space
based approaches. Finally, from the perspective of dynamic traffic management, it presents
the corresponding work and experience of traffic informatization in Shanghai.
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Edited by Dr. Ahmed Abdel-Rahim
ISBN 978-953-51-0347-9
Hard cover, 206 pages
Publisher InTech
Published online 16, March, 2012
Published in print edition March, 2012
Intelligent Transportation Systems (ITS) have transformed surface transportation networks through the
integration of advanced communications and computing technologies into the transportation infrastructure. ITS
technologies have improved the safety and mobility of the transportation network through advanced
applications such as electronic toll collection, in-vehicle navigation systems, collision avoidance systems, and
advanced traffic management systems, and advanced traveler information systems. In this book that focuses
on different ITS technologies and applications, authors from several countries have contributed chapters
covering different ITS technologies, applications, and management practices with the expectation that the
open exchange of scientific results and ideas presented in this book will lead to improved understanding of ITS
technologies and their applications.
How to reference
In order to correctly reference this scholarly work, feel free to copy and paste the following:
Yang Zhang (2012). How to Provide Accurate and Robust Traffic Forecasts Practically?, Intelligent
Transportation Systems, Dr. Ahmed Abdel-Rahim (Ed.), ISBN: 978-953-51-0347-9, InTech, Available from:
http://www.intechopen.com/books/intelligent-transportation-systems/how-to-provide-accurate-and-robust-traffic-
InTech Europe
University Campus STeP Ri
Slavka Krautzeka 83/A
51000 Rijeka, Croatia
Phone: +385 (51) 770 447
Fax: +385 (51) 686 166
www.intechopen.com
InTech China
Unit 405, Office Block, Hotel Equatorial Shanghai
No.65, Yan An Road (West), Shanghai, 200040, China
Phone: +86-21-62489820
Fax: +86-21-62489821
forecasts-practically-

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